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Research On Key Control Technology And System Of Smart Home

Posted on:2020-01-05Degree:MasterType:Thesis
Country:ChinaCandidate:K Y WangFull Text:PDF
GTID:2392330599451262Subject:Engineering
Abstract/Summary:PDF Full Text Request
With the improvement of people's living standards,as well as the rapid development of computer network technology,control technology,sensor technology and artificial intelligence,intelligent home life has become the trend of development.At present,there are still some problems with the existing smart home products,such as incomplete functions,inconvenient operation,incompatibility between brands,etc.At the same time,there is still much room for improvement in terms of energy saving and intelligence.In view of above problems,this paper carried out the following research work on intelligent home intelligent adaptive control.1)In the research of smart home user behavior prediction technology,this paper presents a method for predicting user behavior by using an improved ARIMA model.Taking the opening time of an electrical appliance as an example,the method analyzes the raw data of the opening time of the first 84 days and establishes an improved ARIMA model to predict the time of opening the appliance in the latter 7 days.The comparison result between the predicted value of the improved ARIMA model and that of the ARIMA model shows that the improved ARIMA model has better prediction effect and can achieve accurate short-term prediction.2)In the traditional research of thermal comfort,the thermal comfort equation has the disadvantages of complicated calculation process,large amount of calculation,time-consuming and laborious.therefore,this paper proposes a PMV real-time prediction model,which uses the improved PSO to optimize the RBF neural network.Utilizing the collected experimental data,the model achieves the prediction of thermal comfort index by model experiment emulation.The comparison results among RBF prediction model,prediction model of RBF neural network optimized by PSO and prediction model of RBF neural network optimized by improved PSO show that the proposed prediction model has faster convergence and more accurate prediction.3)In terms of hardware and software design,adopting a convenient wireless network layout,this paper takes development board with Samsung 4412 processor as the core to be the main controller of the system,and uses ZigBee protocol networking,infrared module and sensors to control indoor comfort.At the same time,the smart home appliances can also be controlled anywhere and anytime with the Android smart phone client.
Keywords/Search Tags:Smart home, Intelligence algorithm, ARIMA model, Thermal comfort, ZigBee network
PDF Full Text Request
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